The evidence base for the benefits of urban nature for people and biodiversity is strong. However, cities are diverse and the social and environmental contexts of cities are likely to influence the observed effects of urban nature, and the application of evidence to differing contexts. To explore biases in the evidence base for the effects of urban nature, we text-matched city names in the abstracts and affiliations of 14 786 journal articles, from separate searches for articles on urban biodiversity, the health and wellbeing impacts of urban nature, and on urban ecosystem services. City names were found in 51% of article abstracts and 92% of affiliations. Most large cities were studied many times over, while only a small proportion of small cities were studied once or twice. Almost half the cities studied also had an author with an affiliation from that city. Most studies were from large developed cities, with relatively few studies from Africa and South America in particular. These biases mean the evidence base for the effects of urban nature on people and on biodiversity does not adequately represent the lived experience of the 41% of the world’s urban population who live in small cities, nor the residents of the many rapidly urbanising areas of the developing world. Care should be taken when extrapolating research findings from large global cities to smaller cities and cities in the developing world. Future research should encourage research design focussed on answering research questions rather than city selection by convenience, disentangle the role of city size from measures of urban intensity (such as population density or impervious surface cover), avoid gross urban-rural dualisms, and better contextualise existing research across social and environmental contexts.
ContextTasmania has been called the roadkill capital of Australia. However, little is known about the population-level impact of vehicle mortality on native mammals in the island state. AimsThe aims were to investigate the predictability of roadkill on a given route, based on models of species distribution and live animal abundance for three marsupial species in Tasmania – the Tasmanian pademelon (Thylogale billardierii), Bennett’s wallaby (Macropus rufogriseus) and the bare-nosed wombat (Vombatus ursinus) – and to assess the possibility of predicting the magnitude of state-wide road mortality based on live animal abundance. MethodsRoad mortality of the three species was measured on eight 15-km road segments in south-eastern Tasmania, during 16 weeks over the period 2016–17. Climate suitability was predicted using state-wide geographical location records, using species distribution models, and counts of these species from 190 spotlight survey roads. Key resultsThe Tasmanian pademelons were the most frequently killed animal encountered over the study period. Live abundance, predicted by fitting models to spotlight counts, did not correlate with this fatality rate for any species. However, the climate suitability index generated by the species distribution models was strongly predictive for wombat roadkill, and moderately so for pademelons. ConclusionsAlthough distributional and wildlife abundance records are commonly available and well described by models based on climate, vegetation and land-use predictors, this approach to climate suitability modelling has limited predictability for roadkill counts on specific routes. ImplicationsRoad-specific factors, such as characteristics of the road infrastructure, nearby habitats and behavioural traits, seem to be required to explain roadkill frequency. Determining their relative importance will require spatial analysis of roadkill locations.
Context Vehicle collisions with wildlife can injure or kill animals, threaten human safety, and threaten the viability of rare species. This has led to a focus in road-ecology research on identifying the key predictors of ‘road-kill’ risk, with the goal of guiding management to mitigate its impact. However, because of the complex and context-dependent nature of the causes of risk exposure, modelling road-kill data in ways that yield consistent recommendations has proven challenging. Aim Here we used a multi-model machine-learning approach to identify the spatio-temporal predictors, such as traffic volume, road shape, surrounding vegetation and distance to human settlements, associated with road-kill risk. Methods We collected data on the location, identity and wildlife body size of each road mortality across four seasons along eight roads in southern Tasmania, a ‘road-kill hotspot’ of management concern. We focused on three large-bodied and frequently affected crepuscular Australian marsupial herbivore species, the rufous-bellied pademelon (Thylogale billardierii), Bennett’s wallaby (Macropus rufogriseus) and the bare-nosed wombat (Vombatus ursinus). We fit the point-location data using ‘lasso-regularisation’ of a logistic generalised linear model (LL-GLM) and out-of-bag optimisation of a decision-tree-based ‘random forests’ (RF) algorithm for optimised predictions. Results The RF model, with high-level feature interactions, yielded superior out-of-sample prediction results to the linear additive model, with a RF classification accuracy of 84.8% for the 871 road-kill observations and a true skill statistic of 0.708, compared with 61.2% and 0.205 for the LL-GLM. The lasso rejected road visibility and human density, ranking roadside vegetation type and presence of barrier fencing as the most influential predictors of road-kill locality. Conclusions Forested areas with no roadside barrier fence along curved sections of road posed the highest risk to animals. Seasonally, the frequency of wildlife–vehicle collisions increased notably for females during oestrus, when they were more dispersive and so had a higher encounter rate with roads. Implications These findings illustrate the value of using a combination of attributive and predictive modelling using machine learning to rank and interpret a complexity of possible predictors of road-kill risk, as well as offering a guide to practical management interventions that can mitigate road-related hazards.
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